Human brain clearance imaging: Pathways taken by magnetic resonance imaging contrast agents after administration in cerebrospinal fluid and blood.

Over the last decade, it has become evident that cerebrospinal fluid (CSF) plays a pivotal role in brain solute clearance through perivascular pathways and interactions between the brain and meningeal lymphatic vessels. Whereas most of this fundamental knowledge was gained from rodent models, human brain clearance imaging has provided important insights into the human system and highlighted the existence of important interspecies differences. Current gold standard techniques for human brain clearance imaging involve the injection of gadolinium-based contrast agents and monitoring their distribution and clearance over a period from a few hours up to 2 days. With both intrathecal and intravenous injections being used, which each have their own specific routes of distribution and thus clearance of contrast agent, a clear understanding of the kinetics associated with both approaches, and especially the differences between them, is needed to properly interpret the results. Because it is known that intrathecally injected contrast agent reaches the blood, albeit in small concentrations, and that similarly some of the intravenously injected agent can be detected in CSF, both pathways are connected and will, in theory, reach the same compartments. However, because of clear differences in relative enhancement patterns, both injection approaches will result in varying sensitivities for assessment of different subparts of the brain clearance system. In this opinion review article, the "EU Joint Programme - Neurodegenerative Disease Research (JPND)" consortium on human brain clearance imaging provides an overview of contrast agent pharmacokinetics in vivo following intrathecal and intravenous injections and what typical concentrations and concentration-time curves should be expected. This can be the basis for optimizing and interpreting contrast-enhanced MRI for brain clearance imaging. Furthermore, this can shed light on how molecules may exchange between blood, brain, and CSF.

Corrigendum: Sex differences in the genetics of sarcoidosis across European and African ancestry populations.

[This corrects the article DOI: 10.3389/fmed.2023.1132799.].

Phase-contrast magnetic resonance imaging of intracranial and extracranial blood flow in carotid near-occlusion.

PURPOSE: Compare extracranial internal carotid artery flow rates and intracranial collateral use between conventional ≥ 50% carotid stenosis and carotid near-occlusion, and between symptomatic and asymptomatic carotid near-occlusion. METHODS: We included patients with ≥ 50% carotid stenosis. Degree of stenosis was diagnosed on CTA. Mean blood flow rates were assessed with four-dimensional phase-contrast MRI. RESULTS: We included 110 patients of which 83% were symptomatic, and 38% had near-occlusion. Near-occlusions had lower mean internal carotid artery flow (70 ml/min) than conventional ≥ 50% stenoses (203 ml/min, P < .001). Definite use of ≥ 1 collateral was found in 83% (35/42) of near-occlusions and 10% (7/68) of conventional stenoses (P < .001). However, there were no differences in total cerebral blood flow (514 ml/min vs. 519 ml/min, P = .78) or ipsilateral hemispheric blood flow (234 vs. 227 ml/min, P = .52), between near-occlusions and conventional ≥ 50% stenoses, based on phase-contrast MRI flow rates. There were no differences in total cerebral or hemispheric blood flow, or collateral use, between symptomatic and asymptomatic near-occlusions. CONCLUSION: Near-occlusions have lower internal carotid artery flow rates and more collateral use, but similar total cerebral blood flow and hemispheric blood flow, compared to conventional ≥ 50% carotid stenosis.

Cerebral arterial stiffness is linked to white matter hyperintensities and perivascular spaces in older adults - A 4D flow MRI study.

White matter hyperintensities (WMH), perivascular spaces (PVS) and lacunes are common MRI features of small vessel disease (SVD). However, no shared underlying pathological mechanism has been identified. We investigated whether SVD burden, in terms of WMH, PVS and lacune status, was related to changes in the cerebral arterial wall by applying global cerebral pulse wave velocity (gcPWV) measurements, a newly described marker of cerebral vascular stiffness. In a population-based cohort of 190 individuals, 66-85 years old, SVD features were estimated from T1-weighted and FLAIR images while gcPWV was estimated from 4D flow MRI data. Additionally, the gcPWV's stability to variations in field-of-view was analyzed. The gcPWV was 10.82 (3.94) m/s and displayed a significant correlation to WMH and white matter PVS volume (r = 0.29, p < 0.001; r = 0.21, p = 0.004 respectively from nonparametric tests) that persisted after adjusting for age, blood pressure variables, body mass index, ApoB/A1 ratio, smoking as well as cerebral pulsatility index, a previously suggested early marker of SVD. The gcPWV displayed satisfactory stability to field-of-view variations. Our results suggest that SVD is accompanied by changes in the cerebral arterial wall that can be captured by considering the velocity of the pulse wave transmission through the cerebral arterial network.

Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models.

Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1-3) and a diffusion model for the task of brain tumor segmentation (using two segmentation networks, U-Net and a Swin transformer). Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80%-90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Our conclusion is that sharing synthetic medical images is a viable option to sharing real images, but that further work is required. The trained generative models and the generated synthetic images are shared on AIDA data hub.

Cerebral Blood Flow Patterns in Patients With Low-Flow Carotid Artery Stenosis, a 4D-PCMRI Assessment.

BACKGROUND: Compromised cerebral blood flow can contribute to future ischemic events in patients with symptomatic carotid artery disease. However, there is limited knowledge of the effects on cerebral hemodynamics resulting from a reduced internal carotid artery (ICA) blood flow rate (BFR). PURPOSE: Investigate how reduced ICA-BFR, relates to BFR in the cerebral arteries. STUDY TYPE: Prospective. SUBJECTS: Thirty-eight patients, age 72 ± 6 years (11 female). FIELD STRENGTH/SEQUENCE: 3-Tesla, four-dimensional phase-contrast magnetic resonance imaging (4D-PCMRI). ASSESSMENT: Patients with ischemic stroke or transient ischemic attack were evaluated regarding the degree of stenosis. 4D-PCMRI was used to measure cerebral BFR in 38 patients with symptomatic carotid stenosis (≥50%). BFR in the cerebral arteries was assessed in two subgroups based on symptomatic ICA-BFR: reduced ICA-flow (<160 mL/minutes) and preserved ICA-flow (≥160 mL/minutes). BFR laterality was defined as a difference in the paired ipsilateral-contralateral arteries. STATISTICAL TESTS: Patients were grouped based on ICA-BFR (reduced vs. preserved). Statistical tests (independent sample t-test/paired t-test) were used to compare groups and hemispheres. Significance was determined at P < 0.05. RESULTS: The degree of stenosis was not significantly different, 80% (95% confidence interval [CI] = 73%-87%) in the reduced ICA-flow vs. 72% (CI = 66%-76%) in the preserved ICA-flow; P = 0.09. In the reduced ICA-flow group, a significantly reduced BFR was found in the ipsilateral middle cerebral artery and anterior cerebral artery (A1), while significantly increased in the contralateral A1. Retrograde BFR was found in the posterior communicating artery and ophthalmic artery. Significant BFR laterality was present in all paired arteries in the reduced ICA-flow group, contrasting the preserved ICA-flow group (P = 0.14-0.93). DATA CONCLUSIONS: 4D-PCMRI revealed compromised cerebral BFR due to carotid stenosis, not possible to detect by solely analyzing the degree of stenosis. In patients with reduced ICA-flow, collaterals were not sufficient to maintain symmetrical BFR distribution to the two hemispheres. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Cerebral Blood Flow Assessed with Phase-contrast Magnetic Resonance Imaging during Blood Pressure Changes with Noradrenaline and Labetalol: A Trial in Healthy Volunteers.

BACKGROUND: Adequate cerebral perfusion is central during general anesthesia. However, perfusion is not readily measured bedside. Clinicians currently rely mainly on mean arterial pressure (MAP) as a surrogate, even though the relationship between blood pressure and cerebral blood flow is not well understood. The aim of this study was to apply phase-contrast magnetic resonance imaging to characterize blood flow responses in healthy volunteers to commonly used pharmacologic agents that increase or decrease arterial blood pressure. METHODS: Eighteen healthy volunteers aged 30 to 50 yr were investigated with phase-contrast magnetic resonance imaging. Intra-arterial blood pressure monitoring was used. First, intravenous noradrenaline was administered to a target MAP of 20% above baseline. After a wash-out period, intravenous labetalol was given to a target MAP of 15% below baseline. Cerebral blood flow was measured using phase-contrast magnetic resonance imaging and defined as the sum of flow in the internal carotid arteries and vertebral arteries. Cardiac output (CO) was defined as the flow in the ascending aorta. RESULTS: Baseline median cerebral blood flow was 772 ml/min (interquartile range, 674 to 871), and CO was 5,874 ml/min (5,199 to 6,355). The median dose of noradrenaline was 0.17 µg · kg-1 · h-1 (0.14 to 0.22). During noradrenaline infusion, cerebral blood flow decreased to 705 ml/min (606 to 748; P = 0.001), and CO decreased to 4,995 ml/min (4,705 to 5,635; P = 0.01). A median dose of labetalol was 120 mg (118 to 150). After labetalol boluses, cerebral blood flow was unchanged at 769 ml/min (734 to 900; P = 0.68). CO increased to 6,413 ml/min (6,056 to 7,464; P = 0.03). CONCLUSIONS: In healthy, awake subjects, increasing MAP using intravenous noradrenaline decreased cerebral blood flow and CO. These data do not support inducing hypertension with noradrenaline to increase cerebral blood flow. Cerebral blood flow was unchanged when decreasing MAP using labetalol.

Diagnostic separation of conventional ⩾50% carotid stenosis and near-occlusion with phase-contrast MRI.

INTRODUCTION: The aim of this study was to assess sensitivity, specificity and interrater reliability of phase-contrast MRI (PC-MRI) for diagnosing carotid near-occlusion. PATIENTS AND METHODS: Prospective cross-sectional study conducted between 2018 and 2021. We included participants with suspected 50%-100% carotid stenosis on at least one side, all were examined with CT angiography (CTA) and PC-MRI and both ICAs were analyzed. Degree of stenosis on CTA was the reference test. PC-MRI-based blood flow rates in extracranial ICA and intracranial cerebral arteries were assessed. ICA-cerebral blood flow (CBF) ratio was defined as ICA divided by sum of both ICAs and Basilar artery. RESULTS: We included 136 participants. The ICAs were 102 < 50% stenosis, 88 conventional ⩾50% stenosis (31 with ⩾70%), 49 near-occlusion, 12 occlusions, 20 unclear cause of small distal ICA on CTA and one excluded. For separation of near-occlusion and conventional stenoses, ICA flow rate and ICA-CBF ratio had the highest area under the curve (AUC; 0.98-0.99) for near-occlusion. ICA-CBF ratio ⩽0.225 was 90% (45/49) sensitive and 99% (188/190) specific for near-occlusion. Inter-rater reliability for this threshold was excellent (kappa 0.98). Specificity was 94% (29/31) for cases with ⩾70% stenosis. PC-MRI had modest performance for separating <50% and conventional ⩾50% stenosis (highest AUC 0.74), and eight (16%) of near-occlusions were not distinguishable from occlusion (no visible flow). CONCLUSION: ICA-CBF ratio ⩽0.225 on PC-MRI is an accurate and reliable method to separate conventional ⩾50% stenosis and near-occlusion that is feasible for routine use. PC-MRI should be considered further as a potential standard method for near-occlusion detection, to be used side-by-side with established modalities as PC-MRI cannot separate other degrees of stenosis.

Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures.

Atypical femur fractures (AFF) represent a very rare type of fracture that can be difficult to discriminate radiologically from normal femur fractures (NFF). AFFs are associated with drugs that are administered to prevent osteoporosis-related fragility fractures, which are highly prevalent in the elderly population. Given that these fractures are rare and the radiologic changes are subtle currently only 7% of AFFs are correctly identified, which hinders adequate treatment for most patients with AFF. Deep learning models could be trained to classify automatically a fracture as AFF or NFF, thereby assisting radiologists in detecting these rare fractures. Historically, for this classification task, only imaging data have been used, using convolutional neural networks (CNN) or vision transformers applied to radiographs. However, to mimic situations in which all available data are used to arrive at a diagnosis, we adopted an approach of deep learning that is based on the integration of image data and tabular data (from electronic health records) for 159 patients with AFF and 914 patients with NFF. We hypothesized that the combinatorial data, compiled from all the radiology departments of 72 hospitals in Sweden and the Swedish National Patient Register, would improve classification accuracy, as compared to using only one modality. At the patient level, the area under the ROC curve (AUC) increased from 0.966 to 0.987 when using the integrated set of imaging data and seven pre-selected variables, as compared to only using imaging data. More importantly, the sensitivity increased from 0.796 to 0.903. We found a greater impact of data fusion when only a randomly selected subset of available images was used to make the image and tabular data more balanced for each patient. The AUC then increased from 0.949 to 0.984, and the sensitivity increased from 0.727 to 0.849. These AUC improvements are not large, mainly because of the already excellent performance of the CNN (AUC of 0.966) when only images are used. However, the improvement is clinically highly relevant considering the importance of accuracy in medical diagnostics. We expect an even greater effect when imaging data from a clinical workflow, comprising a more diverse set of diagnostic images, are used.

Lymphopenia and high Ki-67 expression in peripheral blood CD4+ and CD8+ T cells associate with progressive sarcoidosis.

BACKGROUND: Early identification of patients at risk for progressive sarcoidosis may improve intervention. High bronchoalveolar lavage fluid (BALF) lymphocytes and peripheral blood (PB) lymphopenia are associated with worse prognosis. The mechanisms behind are not disentangled, and to date, it is not possible to predict disease course with certainty. OBJECTIVES: Insight into the frequency of T regulatory cells (Tregs), proliferating CD4+ and CD8+ T cells in BALF and PB in clinically well-characterised patients, may provide clues to mechanisms behind differences in disease course. METHODS: Nineteen treatment-naïve patients with newly diagnosed sarcoidosis were assessed with BAL and PB samples at diagnosis. From the majority, repeated PB samples were collected over a year after diagnosis. The patients were followed for a median of 3 years and clinical parameters were used to classify patients into resolving, chronic progressive and chronic stable disease. Lymphocyte counts, frequency of Tregs defined as forkhead box protein 3+ (FoxP3+) CD4+T cells, and proliferating CD4+ and CD8+ T cells assessed with Ki-67 were analysed. RESULTS: Eleven patients disclosed a chronic stable, and eight a progressive disease course, no one resolved during the study period. In PB, lower number of lymphocytes associated with chronic progressive disease, an increased frequency of Ki-67+CD4+ and CD8+ T cells, and a tendency towards higher percentage of FoxP3+CD4+ T cells compared with chronic stable patients. CONCLUSION: A reduction of PB lymphocytes despite increased proliferation of CD4+and CD8+ T cells was observed in patients with chronic active compared with chronic stable sarcoidosis, indicating an increased PB lymphocyte turn-over in patients with deteriorating disease. Measurement of PB Tregs, Ki-67+CD4+ and Ki-67+CD8+ T cells may help in predicting sarcoidosis disease course.

Brain Age Prediction Using 2D Projections Based on Higher-Order Statistical Moments and Eigenslices from 3D Magnetic Resonance Imaging Volumes.

Brain age prediction from 3D MRI volumes using deep learning has recently become a popular research topic, as brain age has been shown to be an important biomarker. Training deep networks can be very computationally demanding for large datasets like the U.K. Biobank (currently 29,035 subjects). In our previous work, it was demonstrated that using a few 2D projections (mean and standard deviation along three axes) instead of each full 3D volume leads to much faster training at the cost of a reduction in prediction accuracy. Here, we investigated if another set of 2D projections, based on higher-order statistical central moments and eigenslices, leads to a higher accuracy. Our results show that higher-order moments do not lead to a higher accuracy, but that eigenslices provide a small improvement. We also show that an ensemble of such models provides further improvement.

Optic Nerve Subarachnoid Space Posture Dependency - An MRI Study in Subjects With Normal Tension Glaucoma and Healthy Controls.

Purpose: The purpose of this study was to examine the differences of optic nerve subarachnoid space (ONSAS) volume in patients with normal tension glaucoma (NTG) and healthy controls in different body positions. Methods: Eight patients with NTG and seven healthy controls underwent magnetic resonance imaging (MRI) examinations in head up tilt (HUT) +11 degrees and head down tilt (HDT) -5 degrees positions according to a randomized protocol determining the starting position. The ONSAS volume in both body positions was measured and compared between the two groups. The results were analyzed using a generalized linear model. Results: Between HDT and HUT, the postural ONSAS volume change was dependent on starting position (P < 0.001) and group (P = 0.003, NTG versus healthy). A subgroup analysis of those that were randomized to HUT examination first, coming directly from an upright position, showed that the patients with NTG had significantly larger positional ONSAS volume changes compared to the healthy controls; 121 ± 22 µL vs. 65 ± 37 µL (P = 0.049). Analysis of the ONSAS volume distribution showed different profiles for patients with NTG and healthy controls. Conclusions: There was a significant difference in ONSAS volume change between patients with NTG and healthy subjects when subjected to posture changes, specifically when going from upright to head-down posture. This indicates that patients with NTG had been exposed to a lower ONSAS pressure when they came from the upright posture, which suggests an increased translaminar pressure difference in upright position. This may support the theory that NTG has a dysfunction in an occlusion mechanism of the optic nerve sheath that could cause abnormally negative ONSAS pressures in upright posture.

MRI with generalized diffusion encoding reveals damaged white matter in patients previously hospitalized for COVID-19 and with persisting symptoms at follow-up.

There is mounting evidence of the long-term effects of COVID-19 on the central nervous system, with patients experiencing diverse symptoms, often suggesting brain involvement. Conventional brain MRI of these patients shows unspecific patterns, with no clear connection of the symptomatology to brain tissue abnormalities, whereas diffusion tensor studies and volumetric analyses detect measurable changes in the brain after COVID-19. Diffusion MRI exploits the random motion of water molecules to achieve unique sensitivity to structures at the microscopic level, and new sequences employing generalized diffusion encoding provide structural information which are sensitive to intravoxel features. In this observational study, a total of 32 persons were investigated: 16 patients previously hospitalized for COVID-19 with persisting symptoms of post-COVID condition (mean age 60 years: range 41-79, all male) at 7-month follow-up and 16 matched controls, not previously hospitalized for COVID-19, with no post-COVID symptoms (mean age 58 years, range 46-69, 11 males). Standard MRI and generalized diffusion encoding MRI were employed to examine the brain white matter of the subjects. To detect possible group differences, several tissue microstructure descriptors obtainable with the employed diffusion sequence, the fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, microscopic anisotropy, orientational coherence (Cc) and variance in compartment's size (CMD) were analysed using the tract-based spatial statistics framework. The tract-based spatial statistics analysis showed widespread statistically significant differences (P < 0.05, corrected for multiple comparisons using the familywise error rate) in all the considered metrics in the white matter of the patients compared to the controls. Fractional anisotropy, microscopic anisotropy and Cc were lower in the patient group, while axial diffusivity, radial diffusivity, mean diffusivity and CMD were higher. Significant changes in fractional anisotropy, microscopic anisotropy and CMD affected approximately half of the analysed white matter voxels located across all brain lobes, while changes in Cc were mainly found in the occipital parts of the brain. Given the predominant alteration in microscopic anisotropy compared to Cc, the observed changes in diffusion anisotropy are mostly due to loss of local anisotropy, possibly connected to axonal damage, rather than white matter fibre coherence disruption. The increase in radial diffusivity is indicative of demyelination, while the changes in mean diffusivity and CMD are compatible with vasogenic oedema. In summary, these widespread alterations of white matter microstructure are indicative of vasogenic oedema, demyelination and axonal damage. These changes might be a contributing factor to the diversity of central nervous system symptoms that many patients experience after COVID-19.

Evaluation of Augmentation Methods in Classifying Autism Spectrum Disorders from fMRI Data with 3D Convolutional Neural Networks.

Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years, and recently, different deep learning approaches have been used. Despite this fact, there has not been any investigation regarding how 3D augmentation can help to create larger datasets, required to train deep networks with millions of parameters. In this study, deep learning was applied to derivatives from resting state functional MRI data, to investigate how different 3D augmentation techniques affect the test accuracy. Specifically, resting state derivatives from 1112 subjects in ABIDE (Autism Brain Imaging Data Exchange) preprocessed were used to train a 3D convolutional neural network (CNN) to classify each subject according to presence or absence of autism spectrum disorder. The results show that augmentation only provide minor improvements to the test accuracy.

Efficient Brain Age Prediction from 3D MRI Volumes Using 2D Projections.

Using 3D CNNs on high-resolution medical volumes is very computationally demanding, especially for large datasets like UK Biobank, which aims to scan 100,000 subjects. Here, we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of 3D volumes leads to reasonable test accuracy (mean absolute error of about 3.5 years) when predicting age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 20-50 s using a single GPU, which is two orders of magnitude faster than a small 3D CNN. This speedup is explained by the fact that 3D brain volumes contain a lot of redundant information, which can be efficiently compressed using 2D projections. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs.

Evaluation of inverse treatment planning for gamma knife radiosurgery using fMRI brain activation maps as organs at risk.

BACKGROUND: Stereotactic radiosurgery (SRS) can be an effective primary or adjuvant treatment option for intracranial tumors. However, it carries risks of various radiation toxicities, which can lead to functional deficits for the patients. Current inverse planning algorithms for SRS provide an efficient way for sparing organs at risk (OARs) by setting maximum radiation dose constraints in the treatment planning process. PURPOSE: We propose using activation maps from functional MRI (fMRI) to map the eloquent regions of the brain and define functional OARs (fOARs) for Gamma Knife SRS treatment planning. METHODS: We implemented a pipeline for analyzing patient fMRI data, generating fOARs from the resulting activation maps, and loading them onto the GammaPlan treatment planning software. We used the Lightning inverse planner to generate multiple treatment plans from open MRI data of five subjects, and evaluated the effects of incorporating the proposed fOARs. RESULTS: The Lightning optimizer designs treatment plans with high conformity to the specified parameters. Setting maximum dose constraints on fOARs successfully limits the radiation dose incident on them, but can have a negative impact on treatment plan quality metrics. By masking out fOAR voxels surrounding the tumor target it is possible to achieve high quality treatment plans while controlling the radiation dose on fOARs. CONCLUSIONS: The proposed method can effectively reduce the radiation dose incident on the eloquent brain areas during Gamma Knife SRS of brain tumors.

Peripheral blood lymphopenia in sarcoidosis associates with HLA-DRB1 alleles but not with lung immune cells and organ involvement.

Different human leukocyte antigen (HLA) alleles associate with disease phenotypes in sarcoidosis. Peripheral blood (PB) lymphopenia is reported as more common in sarcoidosis patients with worse prognosis. The mechanisms behind are unrecognized but a PB depletion due to lymphocytes migrating to lung and/or extra pulmonary organs has been suggested. Insights into associations between HLA alleles, lung immune cells, clinical phenotype including extra pulmonary manifestations (EPM), and PB lymphopenia may provide mechanistic clues and enable adequate intervention in this patient group. In this situdy,141 treatment naïve, newly diagnosed patients were retrospectively identified in a Swedish cohort of sarcoidosis patients. Data on HLA-DRB1 alleles, lung immune cells from bronchoalveolar lavage fluid (BALF), PB lymphocytes and clinical parameters including treatment and disease course (chronic vs. resolving) were collected. The patients were followed for 2 years. PB lymphopenia associated with male sex, development of non-resolving disease, a need for first- and second-line systemic immunosuppressant treatment and HLA- DRB1*07. No correlation between BALF and PB lymphocytes, and no difference in EPM was detected between patients with and without PB lymphopenia. In conclusion, PB lymphopenia is associated with a more severe disease phenotype and carriage of the HLA-DRB1*07 allele. The results do not lend support to the hypothesis about sarcoidosis PB lymphopenia being due to a migration of PB lymphocytes to other organs. Rather, they provide a basis for future studies on the connection between HLA-DRB1*07 and PB lymphopenia mechanisms.

Sex differences in the genetics of sarcoidosis across European and African ancestry populations.

Background: Sex differences in the susceptibility of sarcoidosis are unknown. The study aims to identify sex-dependent genetic variations in two clinical sarcoidosis phenotypes: Löfgren's syndrome (LS) and non-Löfgren's syndrome (non-LS). Methods: A meta-analysis of genome-wide association studies was conducted on Europeans and African Americans, totaling 10,103 individuals from three population-based cohorts, Sweden (n = 3,843), Germany (n = 3,342), and the United States (n = 2,918), followed by an SNP lookup in the UK Biobank (UKB, n = 387,945). A genome-wide association study based on Immunochip data consisting of 141,000 single nucleotide polymorphisms (SNPs) was conducted in the sex groups. The association test was based on logistic regression using the additive model in LS and non-LS sex groups independently. Additionally, gene-based analysis, gene expression, expression quantitative trait loci (eQTL) mapping, and pathway analysis were performed to discover functionally relevant mechanisms related to sarcoidosis and biological sex. Results: We identified sex-dependent genetic variations in LS and non-LS sex groups. Genetic findings in LS sex groups were explicitly located in the extended Major Histocompatibility Complex (xMHC). In non-LS, genetic differences in the sex groups were primarily located in the MHC class II subregion and ANXA11. Gene-based analysis and eQTL enrichment revealed distinct sex-specific gene expression patterns in various tissues and immune cell types. In LS sex groups, a pathway map related to antigen presentation machinery by IFN-gamma. In non-LS, pathway maps related to immune response lectin-induced complement pathway in males and related to maturation and migration of dendritic cells in skin sensitization in females were identified. Conclusion: Our findings provide new evidence for a sex bias underlying sarcoidosis genetic architecture, particularly in clinical phenotypes LS and non-LS. Biological sex likely plays a role in disease mechanisms in sarcoidosis.

m-NLP Inference Models Using Simulation and Regression Techniques.

Current inference techniques for processing multi-needle Langmuir probe (m-NLP) data are often based on adaptations of the Orbital Motion-Limited (OML) theory which relies on several simplifying assumptions. Some of these assumptions, however, are typically not well satisfied in actual experimental conditions, thus leading to uncontrolled uncertainties in inferred plasma parameters. In order to remedy this difficulty, three-dimensional kinetic particle in cell simulations are used to construct a synthetic data set, which is used to compare and assess different m-NLP inference techniques. Using a synthetic data set, regression-based models capable of inferring electron density and satellite potentials from 4-tuples of currents collected with fixed-bias needle probes similar to those on the NorSat-1 satellite, are trained and validated. The regression techniques presented show promising results for plasma density inferences with RMS relative errors less than 20%, and satellite potential inferences with RMS errors less than 0.2 V for potentials ranging from -6 to -1 V. The new inference approaches presented are applied to NorSat-1 data, and compared with existing state-of-the-art inference techniques.